Fuzzy Logic with Engineering Applications, 4th Edition

Fuzzy Logic with Engineering Applications, 4th Edition

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Book description

Fuzzy Logic with Engineering Applications, Fourth Edition

Timothy J. Ross, University of New Mexico, USA

The latest update on this popular textbook

The importance of concepts and methods based on fuzzy logic and fuzzy set theory has been rapidly growing since the early 1990s and all the indications are that this trend will continue in the foreseeable future. Fuzzy Logic with Engineering Applications, Fourth Edition is a new edition of the popular textbook with 15% of new and updated material. Updates have been made to most of the chapters and each chapter now includes new end-of-chapter problems.

The book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.

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Table of contents

  1. Cover
  2. Title Page
  3. About the Author
  4. Preface to the Fourth Edition
  5. 1 Introduction
    1. The Case for Imprecision
    2. A Historical Perspective
    3. The Utility of Fuzzy Systems
    4. Limitations of Fuzzy Systems
    5. The Illusion: Ignoring Uncertainty and Accuracy
    6. Uncertainty and Information
    7. Fuzzy Sets and Membership
    8. Chance versus Fuzziness
    9. Intuition of Uncertainty: Fuzzy versus Probability
    10. Sets as Points in Hypercubes
    11. Summary
    12. References
    13. Problems
    1. Classical Sets
    2. Fuzzy Sets
    3. Summary
    4. References
    5. Problems
    1. Cartesian Product
    2. Crisp Relations
    3. Fuzzy Relations
    4. Tolerance and Equivalence Relations
    5. Fuzzy Tolerance and Equivalence Relations
    6. Value Assignments
    7. Other Forms of the Composition Operation
    8. Summary
    9. References
    10. Problems
    1. Features of the Membership Function
    2. Various Forms
    3. Fuzzification
    4. Defuzzification to Crisp Sets
    5. λ‐Cuts for Fuzzy Relations
    6. Defuzzification to Scalars
    7. Summary
    8. References
    9. Problems
    1. Part I: Logic
    2. Classical Logic
    3. Fuzzy Logic
    4. Part II: Fuzzy Systems
    5. Summary
    6. References
    7. Problems
    1. Membership Value Assignments
    2. Intuition
    3. Inference
    4. Rank Ordering
    5. Neural Networks
    6. Genetic Algorithms
    7. Inductive Reasoning
    8. Summary
    9. References
    10. Problems
    1. Definitions
    2. Batch Least Squares Algorithm
    3. Recursive Least Squares Algorithm
    4. Gradient Method
    5. Clustering Method
    6. Learning from Examples
    7. Modified Learning from Examples
    8. Summary
    9. References
    10. Problems
    1. Fuzzy Relational Equations
    2. Nonlinear Simulation Using Fuzzy Systems
    3. Fuzzy Associative Memories (FAMs)
    4. Summary
    5. References
    6. Problems
    1. Fuzzy Synthetic Evaluation
    2. Fuzzy Ordering
    3. Nontransitive Ranking
    4. Preference and Consensus
    5. Multiobjective Decision Making
    6. Fuzzy Bayesian Decision Method
    7. Decision Making under Fuzzy States and Fuzzy Actions
    8. Summary
    9. References
    10. Problems
    1. Fuzzy Classification
    2. Classification by Equivalence Relations
    3. Cluster Analysis
    4. Cluster Validity
    5. c‐Means Clustering
    6. Hard c‐Means (HCM)
    7. Fuzzy c‐Means (FCM)
    8. Classification Metric
    9. Hardening the Fuzzy c‐Partition
    10. Similarity Relations from Clustering
    11. Fuzzy Pattern Recognition
    12. Single‐Sample Identification
    13. Multifeature Pattern Recognition
    14. Summary
    15. References
    16. Problems
    1. Control System Design Problem
    2. Examples of Fuzzy Control System Design
    3. Fuzzy Engineering Process Control
    4. Fuzzy Statistical Process Control
    5. Industrial Applications
    6. Summary
    7. References
    8. Problems
    1. Fuzzy Optimization
    2. Fuzzy Cognitive Mapping
    3. Agent‐Based Models
    4. Fuzzy Arithmetic and the Extension Principle
    5. Fuzzy Algebra
    6. Data Fusion
    7. Summary
    8. References
    9. Problems
    1. Monotone Measures
    2. Belief and Plausibility
    3. Evidence Theory
    4. Probability Measures
    5. Possibility and Necessity Measures
    6. Possibility Distributions as Fuzzy Sets
    7. Possibility Distributions Derived from Empirical Intervals
    8. Summary
    9. References
    10. Problems
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